Designing New Protein Functions Using Deep Learning with David Baker
[HPP] David BakerNovember 20, 20251h 19min
26 connectionsΒ·40 entities in this videoβThe Power of Protein Design
- π‘ Proteins are the fundamental workers in cells, mediating all critical life processes and solving evolutionary challenges.
- π― Unlike traditional methods that modify existing proteins, the goal is to design entirely new proteins from scratch to address modern problems.
- π§ This involves navigating an astronomical number of possible amino acid sequences (10^130) to find functional designs, far beyond what exists in nature.
Deep Learning for De Novo Protein Creation
- π Early efforts used physically-based models to predict protein structures by estimating atomic interactions and finding lowest energy states.
- β‘ The field transitioned to deep learning methods, specifically diffusion models, analogous to image generation AI like DALL-E.
- π¬ These models are trained on 150,000+ known protein structures to progressively remove noise and generate novel, functional protein shapes.
- β The key is conditioning the generative process to design proteins for specific desired functions or targets, such as binding to a particular structure.
Medical Innovations Through Protein Design
- π Designed proteins can block snake venom toxins and activate the immune system for cancer treatment by bringing together cellular receptors.
- π‘ New proteins act as potent blockers for autoimmunity (e.g., TNF signaling) and can be designed to mimic antibody therapeutics.
- π§ Proteins are being developed to target and destroy misfolding proteins associated with neurodegenerative diseases like Alzheimer's (amyloid beta, tau protein).
- π This technology led to the design of the first clinically approved COVID vaccine, utilizing self-assembling protein nanoparticles.
Advancing Technology and Sustainability
- π οΈ De novo designed nanopores can be embedded in membranes or silicon nitride chips for advanced sensing and DNA sequencing.
- π§© Protein switches can change shape in response to external factors, enabling precise control over biological processes, like immune responses.
- π± Proteins are being designed as catalysts for bond-making (e.g., tuberculosis drugs) and bond-breaking (e.g., plastic degradation).
- β¨ New proteins can template the growth of semiconductor materials (zinc oxide) and biominerals (hydroxyapatite) for novel material science.
- βοΈ Efforts include designing proteins to improve photosynthesis and enhance light harvesting for energy applications.
Future Directions and Challenges
- π Current research focuses on developing atomic-level diffusion models for more sophisticated control over protein design, moving beyond amino acid residues.
- π Building general biological models by training on diverse datasets, including metagenomes, is crucial for extending capabilities across various biological interactions.
- β οΈ While powerful, the main challenge is ensuring designs work experimentally and addressing potential unintended consequences or immune responses in therapeutic applications.
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40 entities
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Transcript292 segments
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Whatβs Discussed
Deep learningProtein designDe novo protein designDiffusion modelsGenerative AIProtein structuresMedical applicationsCancer immunotherapyNeurodegenerative diseasesNanoporesDNA sequencingProtein switchesCatalystsPlastic degradationPhotosynthesis
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